Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)

Cloern, James E.; Jassby, Alan D.; Carstensen, Jacob; Bennett, William A.; Kimmerer, Wim; Nally, Ralph Charles Mac; Schoellhamer, David H.; Winder, Monika

doi:10.4319/lo.2012.57.2.0665

Cited by 9 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, while supporting the cautious comment of Cloern et al (2011), we agree with those who have previously used CUSUM in ecological analysis, that comparisons of transitions in time, using CUSUM transformations, are useful for the identification of synchrony between time series.…”

supporting

confidence: 47%

“…In ecology, the application of CUSUM transformations for identifying links between meteorological, hydrological, and ecological patterns has recently been increasing (Adrian et al 2006;Molinero et al 2008;Breaker and Flora 2009;Briceñ o and Boyer 2010), and the combination of CUSUM charts and bootstrapping has been identified as an important tool in regime shift analysis (Andersen et al 2008). Therefore, while supporting the cautious comment of Cloern et al (2011), we agree with those who have previously used CUSUM in ecological analysis, that comparisons of transitions in time, using CUSUM transformations, are useful for the identification of synchrony between time series.…”

mentioning

confidence: 99%

“…In their comment, Cloern et al (2011) develop theoretical evidence that cumulative sum of variability (CUSUM)-transformed variables should not be used to lead to inferences due to the increase of auto-correlation. Indeed, the use of statistical tools based on the independency between variables is misleading.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Rejoinder to “Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)”

Lancelot

Grosjean

Rousseau

et al. 2012

Limnology & Oceanography

View full text Add to dashboard Cite

supporting

confidence: 47%

mentioning

confidence: 99%

See 1 more Smart Citation

Rejoinder to “Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)”

Lancelot

Grosjean

Rousseau

et al. 2012

Limnology & Oceanography

View full text Add to dashboard Cite

“…This was particularly interesting because of the high concentrations of NH + 4 in the Sacramento River: the correlation between potential nitrification and NH + 4 was positive and at the margins of significance (ρ = 0.71, p = 0.088). Due to the recent controversy over the ecological impacts of elevated NH + 4 concentrations in the Sacramento River (Glibert, 2010 ; Brooks et al, 2012 ; Cloern et al, 2012 ; Lancelot et al, 2012 ), there is substantial interest in quantifying the rates of NH + 4 transformations throughout the Delta. While previous work has inferred high nitrification rates in the Sacramento River due to longitudinal decreases in NH + 4 and increases in NO − 3 concentrations (Parker et al, 2012 ), no measurements of nitrification rates in the Delta exist to date.…”

Section: Resultsmentioning

confidence: 99%

Benthic ammonia oxidizers differ in community structure and biogeochemical potential across a riverine delta

et al. 2015

View full text Add to dashboard Cite

Nitrogen pollution in coastal zones is a widespread issue, particularly in ecosystems with urban or agricultural watersheds. California's Sacramento-San Joaquin Delta, at the landward reaches of San Francisco Bay, is highly impacted by both agricultural runoff and sewage effluent, leading to chronically high nutrient loadings. In particular, the extensive discharge of ammonium into the Sacramento River has altered this ecosystem by vastly increasing ammonium concentrations and thus changing the stoichiometry of inorganic nitrogen stocks, with potential effects throughout the food web. This debate surrounding ammonium inputs highlights the importance of understanding the rates of, and controls on, nitrogen (N) cycling processes across the delta. To date, however, there has been little research examining N biogeochemistry or N-cycling microbial communities in this system. We report the first data on benthic ammonia-oxidizing microbial communities and potential nitrification rates for the Sacramento-San Joaquin Delta, focusing on the functional gene amoA (which codes for the α-subunit of ammonia monooxygenase). There were stark regional differences in ammonia-oxidizing communities, with ammonia-oxidizing bacteria (AOB) outnumbering ammonia-oxidizing archaea (AOA) only in the ammonium-rich Sacramento River. High potential nitrification rates in the Sacramento River suggested these communities may be capable of oxidizing significant amounts of ammonium, compared to the San Joaquin River and the upper reaches of San Francisco Bay. Gene diversity also showed regional patterns, as well as phylogenetically unique ammonia oxidizers in the Sacramento River. The benthic ammonia oxidizers in this nutrient-rich aquatic ecosystem may be important players in its overall nutrient cycling, and their community structure and biogeochemical function appear related to nutrient loadings. Unraveling the microbial ecology and biogeochemistry of N cycling pathways, including benthic nitrification, is a critical step toward understanding how such ecosystems respond to the changing environmental conditions wrought by human development and climate change.

show abstract

“…The correlations among non-stationary time series are spurious in the sense that the estimated coefficients are statistically significant when there are no real relationships among variables (inflated type I error rate). For example, in the analysis of fisheries time series data, spurious results are often produced by correlating cumulative-sum (CUSUM)-transformed variables because the CUSUM transformation generates nonstationary time series from stationary time series (Cloern et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Finding regulation among seemingly unregulated populations: a practical framework for analyzing multivariate population time series for their interactions

2015

View full text Add to dashboard Cite

The structure of ecological communities is often thought to be strongly influenced by population interactions. The interactions are often labeled as bottom-up and top-down control. Previous approaches to identify these processes often assume each population in the community is itself regulated. Therefore, each time series follows a stationary process. However, complex community structure and a lack of regulation in an individual population can result in inappropriate inferences based on traditional statistical approaches. Here, we introduce a statistical framework to analyze potentially non-stationary time series that are collectively regulated. We demonstrate the method with catch-per-unit-effort time series data of selected populations in the Gulf of Mexico. In the Gulf, we found that most of the time series data, which span 26 years, were non-stationary, thus individually unregulated. Species interaction patterns were location-dependent, but where brown shrimp interacted significantly with other species, we identified significant bottom-up forcing. On the other hand, we find almost no evidence of top-down forcing throughout the study areas.Handling Editor: Pierre Dutilleul. Electronic supplementary materialThe online version of this article (

show abstract

Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)

Cited by 9 publications

References 20 publications

Rejoinder to “Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)”

Rejoinder to “Perils of correlating CUSUM‐transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)”

Benthic ammonia oxidizers differ in community structure and biogeochemical potential across a riverine delta

Finding regulation among seemingly unregulated populations: a practical framework for analyzing multivariate population time series for their interactions

Contact Info

Product

Resources

About